Area extraction program, character recognition program, and character recognition device

ABSTRACT

An area extraction method including obtaining a character lattice showing a connection relation between unit areas, which are obtained by separating a character string pattern in an image into patterns each recognized as corresponding to a single character, judging whether or not all combinations of each of the unit areas in the obtained character lattice and each of the unit areas in a regular lattice defining a regular connection relation between the unit areas are likely to be established, generating a path coupling between nodes corresponding to the combination of the unit areas which is determined as likely to be established, determining an optimum path from the generated paths based on a degree of coincidence with the regular lattice or the character lattice, and extracting from an image the unit areas in the character lattice corresponding to the determined optimum path.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority fromthe prior Japanese Patent Application No. 2008-030887, filed on Feb. 12,2008, the entire contents of which are incorporated herein by reference.

BACKGROUND

1. Field

The present invention relates to a character recognition method and anarea extraction method used for character recognition.

2. Description of the Related Art

In the prior art, there has been widely known an OCR (Optical CharacterReader) for capturing a document such as a business form by a scanner toconvert the captured document into image data, and thus to recognize apattern in the image data as a character. In this OCR, an area taken outas a pattern corresponding to a single character is incorrectlyseparated, or a character corresponding to a pattern in each of theseparated areas is not correctly recognized, and therefore, the resultof character recognition is not always reliable. Particularity, when thequality of the image data is bad, or when a word constituted of similarcharacters such as a numeric character is included in image data,accuracy of the character recognition tends to be degraded.

For example, in the method proposed in Japanese Patent Laid-OpenPublication No. 11-272804, the result of the character recognition isamended while being compared with words previously registered in adictionary, whereby the accuracy of the character recognition isenhanced. Specifically, when the result of the character recognition ofa word string having a hierarchical structure and constituted of aplurality of words, such as an address, is compared with wordsregistered in a dictionary, a combination of words with the highestreliability is selected by considering the connection between thehierarchies and thus is determined as a final recognition result.

Further, for example, Japanese Patent Laid-Open Publication No.2002-312365 proposes to retrieve the final recognition result byconsidering a plurality of possibilities in the result of the characterrecognition. Specifically, after a pattern including a character stringis subjected to character recognition, the result of the characterrecognition is subjected to morphological analysis, and the area judgedas a noun or an unregistered word is again subjected to the characterrecognition. The result of the character recognition obtained again isthen added as a candidate to a first character recognition result, andthe final recognition result is retrieved from a plurality of thecandidates.

In general, many business forms include a plurality of informationrepresented by regular expression with a fixed format, such as date andprice. In this information, while the format is the same even if thebusiness form is different, the number of digits of a numeric characteris varied in each of the business forms, and therefore the number ofcharacters may be different. Thus, when the character recognition isapplied to a document such as a business form, a wild card in which thenumber of characters varies is required to be included, and, at the sametime, information expressed by the regular expression is required to becorrectly recognized.

However, when the number of characters in information varies, even ifthe format is fixed, there is a problem that it is difficult to performaccurate character recognition. Namely, when the number of characters inthe information varies, in addition to an error in recognition of thecharacters, a pattern corresponding to a single character may be falselyseparated. Thus, even when the information is expressed by the regularexpression, there is a fixed limit in the enhancement of the accuracy ofthe character recognition. In the methods described in the above patentdocuments, although the words registered in a dictionary or a resultobtained by performing again the character recognition is a candidate ofthe recognition result, the number of candidates is likely to increase.Particularly, when information of a character recognition target is, forexample, a date, many similar numeric characters are included in thedate, and, at the same time, the number of candidates of the recognitionresult is considered to be very large. Therefore, there arises the needto select the final recognition result from the many candidates, wherebya fixed limit occurs in the enhancement of the recognition accuracy.

In addition, when the number of the characters in the informationvaries, even if noise is included in an area corresponding to thisinformation, the noise cannot be efficiently removed. Namely, when thenumber of characters is fixed, the character recognition can beperformed while relatively efficiently removing noise at both ends of acharacter string pattern. However, when the number of the characters inthe information varies, it is difficult to discriminate whether dirt orthe like at the both ends of the character string pattern is noise or acharacter.

SUMMARY

Various embodiments of the present invention provide an area extractionmethod including obtaining a character lattice showing a connectionrelation between unit areas, which are obtained by separating acharacter string pattern in an image into patterns each recognized ascorresponding to a single character, judging whether or not allcombinations of each of the unit areas in the obtained character latticeand each of the unit areas in a regular lattice defining a regularconnection relation between the unit areas are likely to be established,generating a path coupling between nodes corresponding to thecombination of the unit areas which is determined as likely to beestablished, determining an optimum path from the generated paths basedon a degree of coincidence with the regular lattice or the characterlattice, and extracting from an image the unit areas in the characterlattice corresponding to the determined optimum path.

Various embodiments of the present invention provide a characterrecognition device including an acquisition part acquiring a characterlattice showing a connection relation between unit areas, which areobtained by separating a character string pattern in an image intopatterns each recognized as corresponding to a single character, ajudgment part judging whether or not all combinations of each of theunit areas in the character lattice acquired by the acquisition part andeach of the unit areas in a regular lattice defining a regularconnection relation between the unit areas are likely to be established,a generating part generating a path coupling between nodes correspondingto the combination of the unit areas, which is determined as likely tobe established by the judging part, a determining part determining anoptimum path from the paths generated by the generating part based on adegree of coincidence with the regular lattice or the character lattice,an extraction part extracting, from an image, the unit areas in thecharacter lattice corresponding to the optimum path determined by thedetermining part, and a recognition part applying character recognitionto a pattern in the unit area, extracted by the extraction part, withuse of a dictionary for categories, including only characters in acategory to which the unit areas in the regular lattice belong.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram showing a configuration of a relevant part ofa character recognition device according to one embodiment;

FIG. 2 is a view showing an example of a regular lattice according toone embodiment;

FIG. 3 is a view showing an example of a preprocessing result accordingto one embodiment;

FIG. 4 is a view showing an example of a character lattice according toone embodiment;

FIG. 5 is a view showing an example of a correspondence table accordingto one embodiment;

FIG. 6 is a view showing an example of an optimum path according to oneembodiment;

FIG. 7 is a view showing an example of a target area according to oneembodiment;

FIG. 8 is a flow diagram showing an operation of the characterrecognition device according to one embodiment;

FIG. 9 is a flow diagram showing a correspondence table generationprocessing according to one embodiment;

FIG. 10 is flow diagram showing a path generation processing accordingto one embodiment;

FIG. 11 is a view showing an example of coupling of nodes according toone embodiment;

FIG. 12 is a view showing another example of coupling of nodes accordingto one embodiment;

FIG. 13 is a view showing still another example of coupling of nodesaccording to one embodiment; and

FIG. 14 is a view showing an example of a result of characterrecognition according to one embodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

A feature of the embodiment to be hereinafter described is to calculatea reliability for each combination of unit areas in a character lattice,in which a character string area as a target for character recognitionis expressed in lattice form, and unit areas in a regular lattice inwhich a regular expression defining information format is expressed inthe lattice form, and thus to determine an optimum path from pathscoupling nodes corresponding to the combination with a high reliability,whereby an area that corresponds to the optimum path and will undergothe character recognition is extracted. Hereinafter, one embodiment ofthe invention is described in detail with reference to the drawings.

FIG. 1 is a block diagram showing a configuration of a relevant part ofa character recognition device 100 according to one embodiment. Thecharacter recognition device 100 has a preprocessing part 101, adictionary storage part 102 for characters, a regular lattice storagepart 103, a dictionary storage part 104 for categories, a targetcharacter string extraction part 105, a character string areaenlargement part 106, a character lattice generation part 107, arecognition reliability calculation part 108, a path generation part109, an optimum path determination part 100, a target area extractionpart 111, and a recognition part 112 for categories.

The preprocessing part 101 applies the character recognition, in which adictionary for characters stored in the dictionary storage part 102 forcharacters is used, to a business form including a pattern ofinformation expressed by regular expression, such as a date, and obtainsa character string expressed by a character string pattern of the entireimage of the business form.

The dictionary storage part 102 for characters stores a dictionary forcharacters including all characters that can be included in the businessform image. Namely, in the normal character recognition, the dictionarystorage part 102 stores the dictionary for characters used forcomparison with a pattern in an image.

The regular lattice storage part 103 stores a regular lattice definingthe regular expression in the business form. Namely, the regular latticestorage part 103 stores a single character of a unit area in theinformation expressed by the regular expression and the regular latticedefining the connection relation between the unit areas. Specifically,the regular lattice storage part 103, as shown in, for example, FIG. 2,stores a single character of unit areas e1 to e11 in a date and theconnection relation between these unit areas. Hereinafter, thedescription proceeds using a regular lattice of FIG. 2 as an example.

In an example shown in FIG. 2, the unit areas e1 to e4 respectivelycorrespond to numeric characters showing a year, and the unit area e5corresponds to a character

(year)”. The unit areas e6 and e7 correspond to a numeric charactershowing a month, and the unit area e8 corresponds to a character

(month)”. The unit areas e9 and e10 correspond to a numeric charactershowing a day, and the unit area e11 corresponds to a character

(day)”. For example, when a year is expressed by the Christian Era, thedigit number is 4, and therefore, all the unit areas e1 to e4 correspondto a numeric character. When the year is expressed by the name of anera, the digit number is 2, and therefore, only the unit areas e1 and e2correspond to a numeric character. Thus, when the year is expressed bythe name of an era, the unit area e5 connects to the unit area e2.Likewise, since a month and a day have 1 or 2 figures, the unit area e8or e11 may be directly connected to the unit area e6 or e9, or the unitarea e8 or e11 may be connected to the unit areas e6 and e7 or the unitareas e9 and e10.

The dictionary storage part 104 for categories classifies the character,included in the dictionary for characters, by categories and stores adictionary for categories in which characters are included for eachcategory. Namely, the dictionary storage part 104 stores a plurality ofthe dictionaries respectively belonging to different categories, such asa dictionary belonging to a category of numeric characters and adictionary belonging to a category of characters used in a date. In FIG.1, although the dictionary storage part 102 for characters and thedictionary storage part 104 for categories are separately provided, theymay be integrated. Further, instead of storing different dictionaries asthe dictionary for characters and the dictionary for categories, onedictionary for characters and a plurality of definitions of categoriesmay be stored.

The target character string extraction part 105 extracts a targetcharacter string, in which at least a part of the form coincides withthe form in the regular lattice stored in the regular lattice storagepart 103, from the character strings obtained as the result of thecharacter recognition by the preprocessing part 101. Specifically, forexample, when a preprocessing result shown at the bottom of FIG. 3 isobtained from the character string pattern shown at the top of FIG. 3, acharacter string 201 recognized as “9

24” coincides with the connection relation between the unit areas e6,e8, e9, and e10 in the regular lattice shown in FIG. 2, and therefore,the target character string extraction part 105 extracts the characterstring 201. In the extraction of the target character string by thetarget character string extraction part 105, the technique described inInternational Publication No. WO 2007/080642 or other techniques can beused.

The character string area enlargement part 106 enlarges an areaincluding a pattern corresponding to the target character stringextracted by the target character string extraction part 105 anddetermines a character string area corresponding to the entireinformation shown by the regular lattice. Specifically, in a patterncorresponding to the target character string, the character string areaenlargement part 106 calculates an average character size of a singlecharacter of the pattern and an average character interval between thepatterns corresponding to a single character and enlarges the areaincluding the pattern corresponding to the target character string inunits of the area size as the sum of them. When a new patterncorresponding to a single character is included in the enlarged area,the character string area enlargement part 106 further enlarges thearea. Thereafter, the character string area enlargement part 106continues the enlargement of the area until a new pattern correspondingto a single character is not included in the enlarged area and, thus,finally obtains the character string area in which the patterncorresponding to a single character does not exist adjacent thereto.

The character lattice generation part 107 applies the characterrecognition, in which the dictionary for characters stored in thedictionary storage part 102 for characters is used, to the pattern inthe character string area obtained by the character string areaenlargement part 106 and generates a character lattice showing a singlecharacter of the unit area in the character string and the connectionrelation between the unit areas. Namely, the character latticegeneration part 107 separates the pattern in the character string areainto the unit areas corresponding to a single character and applies thecharacter recognition to the pattern in each of the unit areas. At thistime, when there are a plurality of candidates for separating the unitarea, the character lattice generation part 107 applies the characterrecognition to the pattern in the unit area for each of the candidates.

Specifically, for example, when the pattern in the character string areashown at the top of FIG. 4 is separated into the unit areas, with regardto the pattern showing

the character lattice generation part 107 executes the characterrecognition for a candidate where the entire pattern is regarded as oneunit area and a candidate where the pattern is separated into unit areas202 and 203. As a result, as shown, for example, at the middle of FIG.4, with regard to the pattern showing

the character lattice generation part 107 obtains the characterrecognition result corresponding to one character

and the character recognition result corresponding to two characters

and

Then, the character lattice generation part 107 regards as the characterlattice the connection relation between the unit areas in each candidatefor separating the unit area. Specifically, the character latticegeneration part 107, as shown, for example, at the bottom of FIG. 4,generates as the character lattice a single character of unit areas r1to r14 in each of the candidates for separating the unit area and theconnection relation between these unit areas. Namely, in the example ofthe character string area shown at the top of FIG. 4, there are a casewhere the pattern showing

is separated into the unit areas r5 and r6 and a case where the patternis regarded as one unit area r14. When the pattern showing

is regarded as one unit area r14, not the unit areas r5 and r6 but theunit area r14 is connected to the unit areas r4 and r7. Thus, thecharacter lattice generation part 107 generates the character latticeshown at the bottom of FIG. 4.

When the character lattice generation part 107 executes the characterrecognition in the unit area, a distance value (for example, a valuesuch as Mahalanobis distance) showing a degree of similarity between thepattern in each of the unit areas and the pattern of the character asthe character recognition result is calculated. As the distance value issmaller, the pattern in the unit area and the pattern of the characterincluded in the dictionary for characters are more similar to eachother, and thus it can be said that the accuracy of the characterrecognition is high.

The recognition reliability calculation part 108 calculates therecognition reliability, showing the reliability in the characterrecognition, for all the character recognition results in the unit areasin the character lattice generated by the character lattice generationpart 107 and, with regard to all combinations of the unit area in thecharacter lattice and the unit areas in the regular lattice, determineswhether or not the unit areas are highly likely to correspond to eachother.

Specifically, with regard to the unit areas r1 to r14 in the characterlattice in the above example, the recognition reliability calculationpart 108 discriminates a category in the case where the unit areas r1 tor14 are assumed to respectively correspond to the unit areas e1 to e11in the regular lattice and executes the character recognition using thedictionary for categories stored in the dictionary storage part 104 forcategories, limiting to the discriminated category. Thus, for example,when the unit area r1 in the character lattice and the unit area e1 inthe regular lattice are assumed to correspond to each other, the unitarea e1 corresponds to the numeric character, and therefore, therecognition reliability calculation part 108 executes the characterrecognition, limiting the pattern of the unit area r1 to the category ofnumeric characters. At this time, the recognition reliabilitycalculation part 108 calculates the distance value showing the degree ofthe similarity between the pattern in each unit area and the pattern ofthe character as the character recognition result.

Likewise, when the unit area r1 in the character lattice and the unitarea e5 in the regular lattice area are assumed to correspond to eachother, the unit area e5 corresponds to the character

and therefore, the recognition reliability calculation part 108recognizes the pattern of the unit area r1 as

and calculates the distance value showing the degree of the similarity.

The recognition reliability calculation part 108 then judges whether ornot the combinations of the unit areas are established on the basis of aratio of the distance value in the case where the dictionary forcharacters calculated by the character lattice generation part 107 isused to the distance value in the case where the dictionary forcategories is used. Namely, the recognition reliability calculation part108 divides the distance value (dc) in the case where the dictionary forcharacters is used by the distance value (d1) in the case where thedictionary for categories is used to calculate the recognitionreliability (=dc/d1) and determines that the combinations with therecognition reliability not less than a predetermined threshold value islikely to be established.

In the above case, if the combination of the unit areas in the characterlattice and the unit areas in the regular lattice is established, it isconsidered that the character recognition result is the same in bothcases where the dictionary for characters is used and where thedictionary for categories is used. Therefore, the distance value in eachcase is the same (dc=d1), and the recognition reliability is 1.Meanwhile, if the combination of the unit area in the character latticeand the unit area in the regular lattice is not established, it isconsidered that the character included in the dictionary for charactersis further similar to the character recognition result. Therefore, thedistance value in the case where the dictionary for characters is usedis not more than the distance value in the case where the dictionary forcategories is used (dc≦d1). As a result, the recognition reliability isnot more than 1, and the lower the accuracy of the character recognitionresult in the case where the dictionary for categories is used (namely,the larger the distance value d1), the smaller the recognitionreliability.

The recognition reliability calculation part 108 determines from therecognition reliability whether or not all the combinations of the unitareas in the regular lattice and the unit areas in the character latticeis likely to be established, generates a correspondence table specifyingthe combinations, which is likely to be established, as a node, andoutputs the correspondence table to the path generation part 109.Specifically, the recognition reliability calculation part 108, as shownin, for example, FIG. 5, outputs the correspondence table, in which thecombinations of the unit areas likely to be established are shown by thenodes (black circles), to the path generation part 109.

In the correspondence table generated by the recognition reliabilitycalculation part 108, the path generation part 109 executes a pathgeneration processing of sequentially coupling the nodes as thecombinations of the unit areas likely to be established. Specifically,the path generation part 109 couples the nodes in the correspondencetable shown in, for example, FIG. 5 in accordance with condition togenerate the path. In the condition, a predetermined number of unitareas connecting to the unit areas corresponding to a newly couplingnode are extracted, and regarding the nodes constituted of the extractedunit areas, the node with the maximum coupling number is coupled to thenewly coupling node. Namely, regarding the nodes constituted of thecombination of the unit area adjacent to the unit area in the regularlattice corresponding to the newly coupling node and the unit areaadjacent to the unit area in the character lattice corresponding to thenewly coupling node, the node passing through the largest number ofnodes is coupled to the newly coupling node.

Meanwhile, when there are a plurality of the nodes with the largestcoupling number, regarding these nodes, the node included in the pathwith the largest reproducing ratio is coupled to the newly couplingnode. The reproducing ratio shows the level of reproducing theconnection relation in the regular lattice. The path generationprocessing by the path generation part 109 is described in detail later.

When the path generation processing for all nodes on the correspondencetable is completed by the path generation part 109, the optimum pathdetermination part 110 determines one optimum path with the largestreproducing ratio showing the level of reproducing the connectionrelation in the regular lattice and with the largest matching ratioshowing the level of fitting to the connection relation in the characterlattice. Namely, the optimum path determination part 110 selects anoptimum path 204 on the correspondence table, as shown, for example, inFIG. 6. In this case, in the optimum path 204, the unit areas arecoupled in the regular lattice so that e1→e2→e3→e4→e5→e6→e8→e9→e11, andtherefore, the connection relation, in which the unit areas areconnected in the regular lattice shown in FIG. 2 so thate1→e2→e3→e4→e5→e6→e8→e9→e10→e11, is completely reproduced. Further, inthe optimum path 204, the unit areas are coupled in the characterlattice so that r1→r2→r3→r4→r14→r8→r9→r10→r11→r12, and therefore,compared with the connection relation in which the unit areas areconnected in the character lattice shown at the bottom of FIG. 4 so thatr1→r2→r3→r4→r14→r7→r8→r9→r10→r→r12→r13, it is found that 10 unit areasexcept for the unit areas r7 and r13 are matched.

The optimum path determination part 110 determines the optimum pathbased on the reproducing ratio and the matching ratio of each path.However, at this time, the optimum path determination part 110 selectsas the optimum path the path with the maximum reproducing ratio. Whenthere are a plurality of paths with the maximum reproducing ratio, thepath with the matching ratio that is the maximum of these paths isdetermined as the optimum path by the optimum path determination part110.

The target area extraction part 111 extracts as the target area tofinally undergo the character recognition the area including all theunit areas in the character lattice corresponding to the optimum pathdetermined by the optimum path determination part 110. Specifically, forexample when the optimum path shown at the top of FIG. 7 is determined,the target area extraction part 111, as shown at the middle of FIG. 7,selects the unit areas r1, r2, r3, r4, r14, r8, r9, r10, r11, and r12 inthe character lattice corresponding to the optimum path and, as shown atthe bottom of FIG. 7, extracts the minimum target area including theselected unit areas. Thus, the target area extraction part 111 extractsthe target area except for a noise pattern 205 at the bottom of FIG. 7.

The character lattice generation part 107 to the target area extractionpart 111 form an area extraction part in the character recognitiondevice 100 according to the present embodiment, the area extraction partextracting an area, which will actually undergo the characterrecognition, from a character string area in a business form image.

The recognition part 112 for categories applies the characterrecognition, in which the dictionary for categories stored in thedictionary storage part 104 for categories is used, to the pattern inthe target area extracted by the target area extraction part 111 andoutputs the recognition result. Namely, the recognition part 112executes the character recognition in which categories are limited,using the dictionary for categories corresponding to a category ofinformation shown by the pattern in the target area.

Next, the operation of the character recognition device 100 having theabove constitution is described with reference to a flow diagram shownin FIG. 8. In the following description, the examples shown in FIGS. 2to 7 are appropriately referred according to need.

First, when a business form image is input to the character recognitiondevice 100, the character recognition for the entire business form imageis executed by the preprocessing part 101 (step S01). As a result of thepreprocessing, the character recognition result including the charactersshown at the bottom of FIG. 3 can be obtained, and the target characterstring in which a part coincides with the regular lattice is extractedfrom the character recognition result, obtained by the preprocessing, bythe target character sting extraction part 105 (step S102). In thisexample, since the target character string 201: “9

24” coincides with the connection relation between the unit areas e6,e8, e9, and e10 in the regular lattices shown in FIG. 2, the targetcharacter string 201 is extracted.

The area on the business form image corresponding to the targetcharacter string 201 is enlarged to the character string area, includingthe entire lump of information shown at the top of FIG. 4, by thecharacter string area enlargement part 106 (step S103). Namely, the areaon the business form image corresponding to the target character string201 is enlarged in units of an area occupied by the patterncorresponding to a single character, and the character string areaincluding all patterns adjacent to the target character string 201 isobtained by the character string area enlargement part 106. In thiscase, the pattern which is a lump of information and shows a date isincluded in the character string area. Further, as shown at the top ofFIG. 4, a noise pattern except for a date is included in the characterstring area.

When the character string area including a lump of information isobtained, the character recognition in which the categories are notlimited is applied to the pattern in the character string area by thecharacter lattice generation part 107 (step S104). Namely, the characterstring area is separated into the unit areas, which include the patterncorresponding to a single character, by the character lattice part 107,and the pattern in each of the unit areas undergoes the characterrecognition using the dictionary for characters stored in the dictionarystorage part 102 for characters. At this time, when there are aplurality of candidates for separating the unit area, the characterlattice generation part 107 executes the character recognition based oneach of the candidates, and, as shown at the middle of FIG. 4, aplurality of candidates of the character recognition result areobtained.

Then, the character lattice showing the connection relation between theunit areas in all the candidates of the character recognition result isgenerated by the character lattice generation part 107 (step S105).Namely, as shown at the bottom of FIG. 4, the character lattice showingthe connection relation in each candidate between the separated unitareas r1 to r14 is generated by the character lattice generation part107. At the same time, the distance value showing the degree ofsimilarity between the character recognition result in each of the unitareas r1 to r14 and the characters included in the dictionary forcharacters is calculated. Hereinafter, the regular lattice and thecharacter lattice are compared with each other, and when the unit areain the regular lattice and the unit area in the character latticecorresponded one-to-one, the combination of the unit areas consistentwith the connection relation in each of the lattices is found.

Namely, the recognition reliability is calculated from the characterrecognition result in each unit area in the character lattice by therecognition reliability calculation part 108, the combination of theunit areas likely to be established is selected based on the recognitionreliability, and a correspondence table generation processing specifyingthe selected combination as the node is executed (step S106). In thiscorrespondence table, as shown in FIG. 5, regarding the combinations ofthe unit areas in the regular lattice and the unit areas in thecharacter lattice, the nodes shown by black circles are recorded inassociation with the combination likely to be established. Thecorrespondence table generation processing by the recognitionreliability calculation part 108 is described in detail later.

Then, the path generation processing of coupling the nodes in thecorrespondence table is executed by the path generation part 109 (stepS107). The path generation processing is started from the node at theend of the correspondence table, and is performed by determining thepreceding node coupling to the present node. The nodes are coupled sothat the connection relation between the unit areas in the regularlattice and the unit areas in the character lattice is more faithfullyreproduced. The path generation processing by the path generation part109 is described in detail later.

When the path generation processing for all the nodes in thecorrespondence table is completed, the optimum path is determined from aplurality of paths, generated in the correspondence table, by theoptimum path determination part 110 (step S108). Namely, the optimumpath with the highest level coinciding with the regular lattice and thecharacter lattice is determined by the optimum path determination part110. Specifically, the path with the maximum reproducing ratio isselected as the optimum path by the optimum path determination part 110.The reproducing ratio shows the rate of the connection relation betweenthe unit areas shown by each path reproducing the connection relationbetween the unit areas in the regular lattice. When there are aplurality of paths with the maximum reproducing ratio, the path with themaximum matching ratio is selected as the optimum path by the optimumpath determination part 110. The matching ratio shows the rate of theconnection relation between the unit areas shown by these paths fittingto the connection relation between the unit areas in the characterlattice.

When the optimum path is determined in the correspondence table, thetarget area, including all the unit areas in the character latticecorresponding to the optimum path, is extracted by the target areaextraction part 111 (step S109). Namely, for example when the optimumpath shown at the top of FIG. 7 is determined, as shown at the middle ofFIG. 7, the unit areas r1, r2, r3, r4, r14, r8, r9, r10, r11, and r12 inthe character lattice are selected by the target area extraction part111, and as shown at the bottom of FIG. 7, the minimum area includingthe selected unit areas is extracted as the target area.

Then, the character recognition in which the dictionary for categoriesabout a date is used is applied to the pattern in the target area by therecognition part 112 for categories (step S110). At this time, in thetarget area shown at the bottom of FIG. 7, since the noise pattern 205included in the character string area shown at the top of FIG. 4 iseliminated, the accuracy of the character recognition can be enhanced.In addition, since the target area is extracted from the optimum path inthe correspondence table, the information expressed by the regularlattice is highly likely to be included in the target area, and thecharacter recognition in which the dictionary for categoriescorresponding to the regular lattice is used is applied to the patternin the target area, whereby the accuracy of the character recognitioncan be further enhanced.

Next, the correspondence table generation processing according to thepresent embodiment is described with reference to a flow diagram shownin FIG. 9.

The character lattice is generated by the character lattice generationpart 107, and when the distance value between the character recognitionresult in each of the unit areas in the character lattice and thecharacter in the dictionary for characters is calculated, the characterrecognition using the dictionary for categories is applied to thepattern in each of the unit areas by the recognition reliabilitycalculation part 108 (step S201). In this case, the dictionary forcategories about the category corresponding to the regular lattice isused, and the pattern in each of the unit areas is limited to, forexample, a numeric character to undergo the character recognition, or islimited to characters

to undergo the character recognition. At the same time, the distancevalue showing the degree of similarity between the character recognitionresult for categories in each of the unit areas and the charactersincluded in the dictionary for categories is calculated.

Then, one combination of the unit areas in the regular lattice and theunit areas in the character lattice is selected by the recognitionreliability calculation part 108 (step S202). In this case, it isassumed that the combination of the unit area e1 and the unit area r1 isselected. When the combination of the unit areas is selected, therecognition reliability about this combination is calculated by therecognition reliability calculation part 108 (step S203). Specifically,the distance value of the character recognition using the dictionary forcharacters is divided by the distance value of the character recognitionusing the dictionary for categories corresponding to the unit area e1and thus the recognition reliability is calculated.

Subsequently, whether or not the recognition reliability is not lessthan a predetermined threshold value is judged (step S204). When therecognition reliability is not less than the predetermined thresholdvalue, the combination of the unit areas is determined as a combinationin which the path is configured on the correspondence table (step S205).When the recognition reliability is less than the predeterminedthreshold value, the combination is determined as a combination in whichthe path is not configured on the correspondence table (step S206). Whenthe combination of the unit area e1 and the unit area r1 is determinedas the combination in which the path is configured, this combination isthe node on the correspondence table. Hereinafter, the node on thecorrespondence table is described in a coordinate form like (e1, r1) byusing the combination of the unit areas.

When the judgment whether or not the combination of the unit area e1 andthe unit area r1 is the node using the recognition reliability iscompleted, it is determined whether or not the judgment of all thecombinations of the unit areas is completed (step S207). At this time,the judgment only for the combination of the unit area e1 and the unitarea r1 is completed, but the judgment for all the combinations is notcompleted (step S207 is “No”). Therefore, another combination of theunit areas (for example, the combination of the unit area e1 and theunit area r2) is selected (step S202). As in the above case, thejudgment whether or not the combination of the unit areas is the node isrepeated, and when the judgment for all the combinations of the unitareas is completed (step S207 is “Yes”), the correspondence table inwhich the combination likely to be established is specified as the nodecompletes (see, FIG. 5).

Next, the path generation processing according to the present embodimentis described with reference to a flow diagram shown in FIG. 10.Hereinafter, the correspondence table shown in FIG. 5 is assumed to begenerated, and the description proceeds using specific examples.

When the correspondence table is generated by the recognitionreliability calculation part 108, one node at the end of thecorrespondence table is selected by the path generation part 109 (stepS301). In this case, (e1, r1) is first selected. Then, whether or notthere is a node preceding (e1, r1) is determined (step S302). There isno unit area connecting before the unit area e1 and the unit area r1,and therefore, it is determined that there is no preceding node (stepS302 is “No”), and thus the next node (e2, r1) is selected (step S301).However, there is no unit area connecting before the unit area r1, andtherefore, it determined that there is no preceding node (step S302 is“No”), and thus (e3, r1) is selected. Hereinafter, in a similar manner,the presence of the preceding node is determined until reaching the nodehaving the preceding node.

When (e2, r2) is selected, the unit area e1 is connected before the unitarea e2, and the unit area r1 is connected before the unit area r2.Therefore, it is determined that there is the preceding node (step S302is “Yes”), and the preceding nodes which correspond to a predeterminednumber of unit areas connected before each of the unit areas areextracted (step S303). When it is assumed that up to two preceding nodeswhich correspond to the unit areas connected to before each of the unitareas are extracted, (e1, r1) corresponding to the unit area e1 beforethe unit area e2 and the unit area r1 before the unit area r2 isextracted.

When the preceding nodes are extracted, one of the extracted precedingnodes and a present selected node is coupled to each other (step S304).However, in the above case, since only (e1, r1) is extracted as thepreceding node, (e1, r1) and (e2, r2) are coupled to each other.

Thereafter, whether or not the selection of all nodes and the couplingof nodes are completed is determined (step S305). When there is anunselected node (step S305 is “No”), the unselected node is newlyselected (step S301). When all nodes are selected (step S305 is “Yes”),it is determined that all the couplable nodes are coupled, and thus thepath generation processing is completed.

Here, the coupling of nodes in the case where the preceding node isextracted is further described using specific examples.

The above-mentioned coupling of nodes is repeated, and when (e4, r4)shown by a white circle in FIG. 11 is selected, four preceding nodes(e2, r2), (e2, r3), (e3, r2), and (e3, r3) corresponding to the unitareas e2 and e3 before the unit area e4 and the unit areas r2 and r3before the unit area r4 are extracted. These preceding nodes aresurrounded by a dashed line in FIG. 11.

In the above case, (e4, r4) is coupled to the preceding node with thelargest coupling number. Namely, while (e2, r2), (e2, r3), and (e3, r2)are respectively coupled to one preceding node, (e3, r3) is coupled totwo preceding nodes. Therefore, (e4, r4) is coupled to (e3, r3).

Further, when (e6, r8) shown by a white circle in FIG. 12 is selected,the unit areas e4 and e5 can be regarded to be connected to each otherbefore the unit area e6, and, at the same time, the unit areas e2 and e5can be regarded to be connected to each other (see, FIG. 2). Likewise,the unit areas r6 and r7 can be regarded to be connected to each otherbefore the unit area r8, and, at the same time, the unit areas r14 andr7 can be regarded to be connected to each other (see, the bottom ofFIG. 4). Thus, when (e6, r8) is selected, the nodes in the rangesurrounded by the dashed line in FIG. 12 are extracted as the precedingnodes.

In the above case, since only one preceding node (e5, r14) exists in therange surrounded by the dashed line in FIG. 12, (e6, r8) is coupled to(e5, r14).

Further, when (e8, r9) shown by a white circle in FIG. 13 is selected,the unit areas e6 and e7 can be regarded to be connected to each otherbefore the unit area e8, and, at the same time, the unit areas e5 and e6can be regarded to be connected to each other (see, FIG. 2). Inaddition, the unit areas r7 and r8 are connected to each other beforethe unit area r9. Thus, when (e8, r9) is selected, the nodes in therange surrounded by the dashed line in FIG. 13 are extracted as thepreceding nodes.

The two preceding nodes (e6, r8) and (e7, r8) are extracted, and eachcoupling number of these preceding nodes is the same. In this case, thepreceding node with a large reproducing ratio of the path reaching eachof the preceding nodes is coupled to (e8, e9). Namely, in the pathreaching (e6, r8), the unit areas are connected so thate1→e2→e3→e4→e5→e6, and thus the connection relation from the unit areae1 to the unit area e6 in the regular lattice is completely reproduced.Meanwhile, in the path reaching (e7, r8), the unit areas are connectedso that e1→e2→e3→e4→e5→e7, and the unit area e6 is not included in theconnection relation. Therefore, the reproducing ratio of the connectionrelation from the unit area e1 to the unit area e7 in the regularlattice is smaller than (e6, r8). Thus, in this case, (e8, r9) iscoupled to (e6, r8) with a larger reproducing ratio.

The node is sequentially coupled to the preceding node, whereby, in viewof the character recognition accuracy and the connection relation in thelattice, all the paths for consistently connecting the combination ofthe unit area in the regular lattice and the unit area in the characterlattice are generated. The path with the maximum reproducing ratio isselected as the optimum one from these paths, whereby the most accuratecorrespondence relation between the unit area in the regular lattice andthe unit area in the character lattice can be obtained. The reproducingratio is a ratio of the number of unit areas in the regular lattice,which correspond to the nodes through which the path passes, to thenumber of unit areas in the connection relation in the regular lattice.For example, with regard to the optimum path shown in FIG. 6, regarding10 unit areas in the connection relation in the regular lattice, inwhich the unit are e7 is not included, the nodes corresponding to allthe 10 unit areas are coupled, and thus the reproducing ratio is 1(=10/10).

When there are a plurality of the paths with the maximum reproducingratio, the path with the maximum matching ratio may be selected as theoptimum path from these paths. The matching ratio is a ratio of thenumber of the unit areas in the character lattice, which correspond tothe nodes through which the path passes, to the number of the unit areasin the connection relation in the character lattice. For example, withregard to the optimum path shown in FIG. 6, regarding 12 unit areas inthe connection relation in the character lattice, in which the unitareas r5 and r6 are not included, the nodes corresponding to 10 unitareas are coupled, and thus the matching ratio is 0.83 (≈10/12).

As described above, according to the present embodiment, on thecorrespondence table in which, regarding all the combinations of theunit areas in the character lattice and the unit areas in the regularlattice, the combination with the recognition reliability not less thana predetermined threshold value is regarded as the node, the pathcoupling the nodes is generated, and the optimum path with the maximumreproducing ratio and the maximum matching ratio is determined.Therefore, the combination of the unit areas consistent with the regularlattice and the character lattice can be determined, whereby, even whena wild card in which the number of characters varies is included in theregular lattice, the correspondence relation between the unit areas canbe accurately determined. In addition, the correspondence relationbetween the unit areas is determined by the determination of the optimumpath, whereby the target area including the unit areas in the characterlattice corresponding to the optimum path is extracted, and thecharacter recognition for categories is applied to the pattern in thetarget area. Therefore, the character recognition can be efficiently andaccurately applied to the information in which the number of charactersvaries and which is expressed by the regular expression.

In the above embodiment, the character recognition for categories isapplied to the pattern in the target area after the extraction of thetarget area. In this case, for example, as shown at the top of FIG. 14,the character recognition for categories is applied also to a noisepattern 301 in the target area. Thus, as shown at the bottom of FIG. 14,the noise pattern 301 is falsely recognized as a character

However, since the recognition accuracy in this character recognitionresult is considered to be low, the area surrounded by a dashed line inwhich the recognition accuracy is not more than a predeterminedreference and may be a blank.

Further, at the time when the optimum path is determined by the optimumpath determination part 110, the correspondence relation between theunit areas in the regular lattice and the unit areas in the characterlattice can be obtained, and therefore, the character recognition forcategories may be executed for each of the unit areas. In this case, asshown at the middle of FIG. 7, since a noise pattern is not included inthe unit areas corresponding to the optimum path, the characterrecognition accuracy can be further enhanced.

In the above embodiment, although the processing of extracting thetarget area and the character recognition processing are executed by thecharacter recognition device 100, these processings are described asprograms with a computer readable form, and it is possible to make acomputer execute these programs.

The embodiments described above are only examples, and it is apparent tothose skilled in the art that the invention can be modified or alteredby combining the component elements of each embodiment. It will also beapparent to those skilled in the art that the embodiments describedabove can be variously modified without departing from the spirit andscope of the invention described in the appended claims.

1. A computer readable recording medium in which a program executing an area extraction method is recorded, the program when executed by a computer causes the computer to perform the method comprising: an obtaining step of obtaining a character lattice showing a connection relation between unit areas, which are obtained by separating a character string pattern in an image into patterns each recognized as corresponding to a single character; a judging step of judging whether or not all combinations of each of the unit areas in the character lattice obtained in the obtaining step and each of the unit areas in a regular lattice defining a regular connection relation between the unit areas are likely to be established; a generating step of generating a path coupling between nodes corresponding to the combination of the unit areas, which is determined as likely to be established in the judging step; a determining step of determining an optimum path from the paths generated in the generating step based on a degree of coincidence with the regular lattice or the character lattice; and an extracting step of extracting, from an image, the unit areas in the character lattice corresponding to the optimum path determined in the determining step.
 2. The area extraction program as claimed in claim 1, wherein the generating step further comprises: a node extracting step of extracting a newly coupling node newly coupled to an other node and a preceding node which has been already coupled to other node and is in the combination of the unit areas in a predetermined range from the unit area corresponding to the newly coupling node in the connection relation between the unit areas shown by the character lattice and the regular lattice; and a node coupling step of coupling the preceding node, which is extracted in the node extracting step and has the largest number of coupling to the preceding node, to the newly coupling node.
 3. The area extraction program as claimed in claim 2, wherein, in the coupling step, when there are a plurality of preceding nodes with the largest coupling number, regarding the corresponding plurality of preceding nodes, the preceding node included in the path and the newly coupling node are coupled to each other based on a reproducing ratio of reproducing the connection relation between the unit areas shown by the regular lattice.
 4. The area extraction program as claimed in claim 1, wherein, in the determining step, the optimum path is determined based on a reproducing ratio of reproducing the connection relation between the unit areas shown by a regular lattice.
 5. The area extraction program as claimed in claim 4, wherein, in the determining step, when there are a plurality of paths with the largest reproducing ratio, regarding the corresponding plurality of paths, the optimum path is determined based on a matching ratio of fitting to the connection relation between the unit areas shown by the character lattice.
 6. The area extraction program as claimed in claim 1, wherein the judging step further comprises: a calculating step of, with respect to patterns in the unit area in the character lattice in each of the combinations of the unit areas, calculating a recognition reliability showing a ratio between a character recognition accuracy in a case where character recognition is executed using a dictionary for characters including all characters and the character recognition accuracy in a case where the character recognition is executed using a dictionary for categories in which only characters in a category, to which the unit areas in the regular lattice belong, is included, wherein a combination in which the recognition reliability calculated in the calculating step meets a predetermined standard is determined to be likely to be established.
 7. A computer readable recording medium, in which a program executing an area extraction method is recorded, the program when executed by a computer causes the computer to perform the method comprising: an obtaining step of obtaining a character lattice showing a connection relation between unit areas, which are obtained by separating a character string pattern in an image into patterns each recognized as corresponding to a single character; a judging step of judging whether or not all combinations of each of the unit areas in the character lattice obtained in the obtaining step and each of the unit areas in a regular lattice defining a regular connection relation between the unit areas are likely to be established; a generating step of generating a path coupling between nodes corresponding to the combination of the unit areas, which is determined as likely to be established in the judging step; a determining step of determining an optimum path from the paths generated in the generating step based on a degree of coincidence with the regular lattice or the character lattice; an extracting step of extracting, from an image, the unit areas in the character lattice corresponding to the optimum path determined in the determining step; and a recognizing step of applying character recognition to a pattern in the unit area, extracted in the extracting step, with use of a dictionary for categories, including only characters in a category to which the unit areas in the regular lattice belong.
 8. A character recognition device comprising: acquisition means for acquiring a character lattice showing a connection relation between unit areas, which are obtained by separating a character string pattern in an image into patterns each recognized as corresponding to a single character; judgment means for judging whether or not all combinations of each of the unit areas in the character lattice acquired by the acquisition means and each of the unit areas in a regular lattice defining a regular connection relation between the unit areas are likely to be established; generating means for generating a path coupling between nodes corresponding to the combination of the unit areas, which is determined as likely to be established by the judging means; determining means for determining an optimum path from the paths generated by the generating means based on a degree of coincidence with the regular lattice or the character lattice; extraction means for extracting, from an image, the unit areas in the character lattice corresponding to the optimum path determined by the determining means; and recognition means for applying character recognition to a pattern in the unit area, extracted by the extraction means, with use of a dictionary for categories, including only characters in a category to which the unit areas in the regular lattice belong.
 9. A character recognition method comprising: an obtaining step of obtaining a character lattice showing a connection relation between unit areas, which are obtained by separating a character string pattern in an image into patterns each recognized as corresponding to a single character; a judging step of judging whether or not all combinations of each of the unit areas in the character lattice obtained in the obtaining step and each of the unit areas in a regular lattice defining a regular connection relation between the unit areas are likely to be established; a generating step of generating a path coupling between nodes corresponding to the combination of the unit areas, which is determined as likely to be established in the judging step; a determining step of determining an optimum path from the paths generated in the generating step based on a degree of coincidence with the regular lattice or the character lattice; an extracting step of extracting from an image the unit areas in the character lattice corresponding to the optimum path determined in the determining step; and a recognizing step of applying character recognition to a pattern in the unit area, extracted in the extracting step, with use of a dictionary for categories, including only characters in a category to which the unit areas in the regular lattice belong.
 10. A character recognition device, comprising: an acquisition part acquiring a character lattice showing a connection relation between unit areas, which are obtained by separating a character string pattern in an image into patterns each recognized as corresponding to a single character; a judgment part judging whether or not all combinations of each of the unit areas in the character lattice acquired by the acquisition part and each of the unit areas in a regular lattice defining a regular connection relation between the unit areas are likely to be established; a generating part generating a path coupling between nodes corresponding to the combination of the unit areas, which is determined as likely to be established by the judging part; a determining part determining an optimum path from the paths generated by the generating part based on a degree of coincidence with the regular lattice or the character lattice; an extraction part extracting, from an image, the unit areas in the character lattice corresponding to the optimum path determined by the determining part; and a recognition part applying character recognition to a pattern in the unit area, extracted by the extraction part, with use of a dictionary for categories, including only characters in a category to which the unit areas in the regular lattice belong. 